doi: 10.1111/1346-8138.13005
Journal of Dermatology 2015; 42: 1149–1152
ORIGINAL ARTICLE
Genomic diagnosis by whole genome sequencing in a Korean family with atypical progeroid syndrome Seungbok LEE,1 Sae Mi PARK,2 Hyun Ji KIM,2 Jin-Wou KIM,2 Dong Soo YU,2 Young Bok LEE2 1
Seoul National University Hospital, 2Department of Dermatology, College of Medicine, The Catholic University of Korea, Seoul, Korea
ABSTRACT Clinical genomic diagnosis is unfamiliar to many dermatologists. Limited knowledge of bioinformatics has limited the use of the next generation sequencing method in dermatological clinics. We evaluated the usefulness of whole genome sequencing as a diagnostic approach to inherited dermatological disease. Here, we present our experience with two female siblings with atypical familial generalized lipodystrophy with diabetes mellitus and dyslipidemia. Whole genome sequencing was performed to diagnose the inherited disease. We compared control genomic databases using the Exome Aggregation Consortium, and filtered false-positive calls with the segmental duplication, non-flagged single nucleotide variants and COSMIC mutation databases, and applied the prediction tools of SIFT and PolyPhen2. The two siblings who presented with generalized lipodystrophy were diagnosed with an atypical progeroid syndrome with a p.D136H mutation in the LMNA gene (NM_005572). We diagnosed a familial atypical progeroid syndrome using whole genome sequencing. In this paper, we present our experience with whole genome sequencing and demonstrate that it can provide useful information for clinical genomic diagnosis of inherited diseases with atypical clinical features, such as atypical progeroid syndrome.
Key words: atypical progeroid syndrome, genomic diagnosis, LMNA mutation, next generation sequencing, whole genome sequencing.
INTRODUCTION There have been significant technical advances in the decade since the Human Genome Project was completed in 2003. Next generation sequencing (NGS) provides a much cheaper and higher throughput alternative to sequencing DNA and can be completed in just a few days. A new era of clinical genomics that identifies genes responsible for inherited skin diseases has begun.1 However, data analysis of NGS, especially whole genome sequencing (WGS), may be difficult for dermatologists because it requires specialized knowledge of bioinformatics to obtain accurate information from sequencing data. Here, we report on two female siblings who presented with generalized fat loss that was suspicious for generalized lipodystrophy. The clinical features of the probands were quite different with previously reported generalized lipodystrophy. We performed WGS to diagnose the inherited disease and to identify genes responsible for the disease.
METHODS Case presentation A 27-year-old Korean female was born with normal weight and showed normal growth and development until she was
11 years old. She reported that generalized fat loss had gradually progressed and she was diagnosed with diabetes mellitus when she was 16 years old. She had difficulties in controlling blood sugar levels. There was abnormal lipid metabolism with serum cholesterol of 520 mg/dL and serum triglycerides of more than 3000 mg/dL. She was 160 cm tall and weighed 30 kg. Prominent fat loss was observed on the face, extremities, trunk, back, palms and soles. No breast tissue was observed. She had no acanthosis nigricans, hyperpigmentation or hypertrichosis, which are clinical characteristics of generalized lipodystrophy (Fig. 1a, left and upper right). The patient reported that permanent teeth had not erupted and she only had several remaining deciduous teeth. The patient reported thin hair and mild hair loss since she was 10 years old. However, she did not have a highpitched voice, scleroderma, osteoporosis, acro-osteolysis, cataract, hypertension or psychiatric disturbance. Her sister presented with the same clinical features, including generalized subcutaneous fat loss, diabetes mellitus and dyslipidemia, and the same dental problem. She was much shorter than her sister (143 cm tall) and weighed 33 kg. Generalized fat loss started when she was 13 years old (Fig. 1a, middle and lower right). She had thin hair; however, she did not report hair loss yet. She had no osteoporosis, cataract or
Correspondence: Young Bok Lee, M.D., Ph.D., Department of Dermatology, College of Medicine, Uijeongbu St Mary’s Hospital, The Catholic University of Korea, 271 Chunbo Street, Uijeongbu-si, Gyeonggi-do 480-717, Korea. Email: [email protected] Received 4 March 2015; accepted 20 May 2015.
© 2015 Japanese Dermatological Association
1149
S. Lee et al.
Patients
(a)
The siblings were enrolled into our study after they consulted the Department of Dermatology at Uijeongbu St Mary’s Hospital. Informed consent was provided by the patients. All of the procedures were performed with the approval of the ethics committee of Uijeongbu St Mary’s Hospital (Kyunggi, Korea; institutional review board no. UC14TISI0122).
WGS and variant calling Genomic DNA was extracted from peripheral blood leukocytes, which underwent library preparation using the TruSeq Nano DNA Sample Preparation Kit (Illumina, San Diego, CA, USA) and paired-end sequencing through the Illumina HiSeq X Ten platform (150 bp in length). We aligned generated sequence reads to the human reference genome (hg19) and called genomic variants under default options using the Isaac aligner and variant caller, respectively. Variant annotation was performed using the ANNOVAR program.2
(b)
Variant filtration and discovery of causal mutations Among the sequence variants called, we selected pathogenic candidates according to several filtration criteria as follows: (i) non-silent single nucleotide variants (SNV) or indels; (ii) allele frequency (AF) of less than 10 4 in the 1000 Genomes Project, Exome Sequencing Project and Exome Aggregation Consortium (ExAC, URL: http://exac.broadinstitute.org), and Complete Genomics sequencing data;3,4 (iii) not located in the segmental duplication regions; (iv) neither non-flagged SNV (dbSNP 138) nor COSMIC mutations (cosmic 70); and (v) predicted to be damaging by SIFT and PolyPhen2.5,6 ClinVar was used to screen previously reported disease- or phenotype-related variants among candidates.7 For the validation of final candidates, we sequenced the target region from the affected sister using PCR and subsequent Sanger sequencing methods (forward primer, GAGGCAAGCAGATGCAAACC; reverse primer, GGACAGGTGAATGGCTCTGAA).
Figure 1. (a) Clinical features of the sisters and (b) pedigree of the probands. Arrow indicates the proband.
psychiatric disturbance. Their mother also had experienced generalized fat loss and diabetes mellitus, and had passed away at the age of 40 years due to complications associated with diabetic mellitus (Fig. 1b). The probands were clinically diagnosed with generalized lipodystrophy with diabetes mellitus; however, the clinical characteristics of the probands were not consistent with familial generalized lipodystrophy that had been previously reported. We performed WGS for the older sister to identify the gene responsible for the inherited disease and sequenced the target region from the younger sister using polymerase chain reaction (PCR) and subsequent Sanger sequencing methods.
RESULTS The sequencing summary is shown in Table 1. Mean read depth was 34.8X and 95.7% of genomic regions were covered by 20 or more reads. For the 3.91 million variants called, we applied stepwise filtrations described in Methods (Fig. 2) and found one variant, rs267607619 (LMNA p.D136H, Fig. 3), which was previously reported to be causative for atypical progeroid
Table 1. Summary of whole genome sequencing
Sample
Total reads
De-duplicated reads
Patient
762 369 890
707 727 844
Mappable reads
Mean depth (9)
% ≥910 coverage
% ≥920 coverage
% ≥930 coverage
SNV
Small insertions
Small deletions
663 027 384
34.8
98.3
95.7
77.2
3 451 965
221 744
240 944
SNV, single nucleotide variant.
1150
© 2015 Japanese Dermatological Association
WGS of Korean APS with LMNA mutation
Figure 2. Stepwise filtration of candidate genes. SNV, silent single nucleotide variant. syndrome in an Indian woman.8 This variant was also listed in the ClinVar database, but its clinical significance was categorized as “untested”.
DISCUSSION The present study details our experience with the first Korean case of atypical progeroid syndrome and proposes an easy and efficient method of WGS data analysis for use in dermatology. Initially, we had difficulty with the clinical diagnosis of two patients presenting with generalized fat loss. With the results of genomic diagnosis, we successfully identified atypical progeroid syndrome using WGS and bioinformatics. WGS revealed an accurate genomic diagnosis identifying an LMNA p.D136H mutation (NM_005572) in a few days. This LMNA p.D136H mutation was the first report in a Korean family with atypical progeroid syndrome. The LMNA p.D136H
mutation was reported in a previous report in an Indian patient.8 The clinical manifestations of the previous case were quite similar to our case, including generalized fat loss and diabetes mellitus, dyslipidemia and onset of disease. However, the previous case was caused by a de novo mutation without family history. The LMNA p.D136H mutation has not been reported other than in the previous Indian case and is classified as “untested” in the ClinVar database. The presenting cases provided meaningful evidence that the LMNA p.D136H mutation has pathogenicity that results in a presentation of generalized lipodystrophy with severe metabolic syndromes. The LMNA gene encodes lamin A/C, which are structural components of the nuclear lamina and nucleoplasmic scaffold and are involved in maintaining nuclear shape and structure. Mutations in the LMNA gene are responsible for diseases of striated muscle, lipodystrophy syndrome, peripheral neuropathy and progeria. Among the progeria syndromes, Hutchinson– Gilford progeria syndrome is a well-recognized typical autosomal dominant progeria due to LMNA gene mutation and characterized by growth retardation, alopecia, loss of subcutaneous fat, and cutaneous, skeletal9 and cardiovascular features.10 Besides Hutchinson–Gilford progeria syndrome, several atypical progeria syndromes with LMNA gene mutations have been reported with various clinical features, such as abnormal lipid metabolism and distribution, osteoporosis or acro-osteolysis.8 Atypical progeroid syndrome is a rare disease and it has been reported in Chinese11 and Japanese populations.12 Although the LMNA mutations have been noted in previously reported cases,11,12 the clinical characteristics were different from the present case. In this case, we diagnosed a Korean family with atypical progeroid syndrome by detecting an LMNA mutation using the WGS method. In diagnosing a rare inherited disease with various clinical features, such as atypical progeroid syndrome, WGS can contribute useful information for diagnosis. Successful discovery of the casual mutations for rare diseases largely depends on the annotation databases used. First, pathogenic candidates may differ slightly according to the choice of gene database. There are several gene annotation databases available, such as RefSeq, Ensembl and UCSC gene sets, and we used the most conservative one, the RefSeq gene database. Second, the sample size of normal healthy controls is very important for discovery of the mutations in rare diseases. In this study of our two patients, we used control
Figure 3. Genetic findings of the patient’s sequencing analysis in this family revealed a p.D136H mutation of the LMNA gene.
© 2015 Japanese Dermatological Association
1151
S. Lee et al.
genomic databases including ExAC, which provides the exomic variant information for more than 60 000 unrelated individuals. In addition, the use of several genomic databases allows for more filtering of false-positive calls. We used the segmental duplication, non-flagged SNV and COSMIC mutation database to exclude false variant calls, and we applied the prediction tools of SIFT and PolyPhen2 to exclude biologically benign or tolerant variants (Fig. 2). Using the strategy described above, this study successfully identified the causal mutation in a patient in a few days (only 1 day after variant calling). However, it is essential that each of the criteria can be modified depending on the situation. For example, if there are too many candidates left after filtration, the AF criteria may need to be stricter in order to leave even rarer variants. If we applied an AF cut-off of 10 6 instead of 10 4 to our patient, 10 variants remained, which still included the LMNA p.D136H mutation. Likewise, use of different prediction tools may also result in different candidates. In particular, if interested in indentifying pathogenic coding indels, most prediction tools are of little help at all because they cover only non-synonymous SNV. Therefore, although the application of high-throughput sequencing technology in clinical diagnostics has become much easier and faster than ever, minor titrations or curations by bioinformaticians may always be necessary for both higher sensitivity and specificity. In our experience, WGS served as a powerful tool for identifying the responsible gene in an inherited dermatological disease. Analysis takes only a few days using stepwise filtration, which may save time with the concept of a 2-day genome analysis.13 We hope that more genes can be identified in inherited dermatological disease using the NGS technique and the pathogenicity of candidate genes can be verified by dermatologists.
ACKNOWLEDGMENTS: The authors would like to thank the Exome Aggregation Consortium and the groups that provided exome variant data for comparison. A full list of contributing groups can be
1152
found at http://exac.broadinstitute.org/about. There was no funding sources.
CONFLICT OF INTEREST:
None declared.
REFERENCES 1 Grada A, Weinbrecht K. Next-generation sequencing: methodology and application. J Invest Dermatol 2013; 133: e11. 2 Wang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res 2010; 38: e164. 3 Genomes Project C, Abecasis GR, Auton A et al. An integrated map of genetic variation from 1,092 human genomes. Nature 2012; 491: 56–65. 4 Drmanac R, Sparks AB, Callow MJ et al. Human genome sequencing using unchained base reads on self-assembling DNA nanoarrays. Science 2010; 327: 78–81. 5 Kumar P, Henikoff S, Ng PC. Predicting the effects of coding nonsynonymous variants on protein function using the SIFT algorithm. Nat Protoc 2009; 4: 1073–1081. 6 Adzhubei IA, Schmidt S, Peshkin L et al. A method and server for predicting damaging missense mutations. Nat Methods 2010; 7: 248–249. 7 Landrum MJ, Lee JM, Riley GR et al. ClinVar: public archive of relationships among sequence variation and human phenotype. Nucleic Acids Res 2014; 42: D980–D985. 8 Garg A, Subramanyam L, Agarwal AK et al. Atypical progeroid syndrome due to heterozygous missense LMNA mutations. J Clin Endocrinol Metab 2009; 94: 4971–4983. 9 Maggi L, D’Amico A, Pini A et al. LMNA-associated myopathies: the Italian experience in a large cohort of patients. Neurology 2014; 83: 1634–1644. 10 Lai CC, Yeh YH, Hsieh WP et al. Whole-exome sequencing to identify a novel LMNA gene mutation associated with inherited cardiac conduction disease. PLoS One 2013; 8: e83322. 11 Guo H, Luo N, Hao F, Bai Y. p.Pro4Arg mutation in LMNA gene: a new atypical progeria phenotype without metabolism abnormalities. Gene 2014; 546: 35–39. 12 Motegi S, Yokoyama Y, Uchiyama A et al. First Japanese case of atypical progeroid syndrome/atypical Werner syndrome with heterozygous LMNA mutation. J Dermatol 2014; 41: 1047–1052. 13 Saunders CJ, Miller NA, Soden SE et al. Rapid whole-genome sequencing for genetic disease diagnosis in neonatal intensive care units. Sci Transl Med 2012; 4: 154ra35.
© 2015 Japanese Dermatological Association
Journal of Dermatology 2015; 42: 1149–1152
ORIGINAL ARTICLE
Genomic diagnosis by whole genome sequencing in a Korean family with atypical progeroid syndrome Seungbok LEE,1 Sae Mi PARK,2 Hyun Ji KIM,2 Jin-Wou KIM,2 Dong Soo YU,2 Young Bok LEE2 1
Seoul National University Hospital, 2Department of Dermatology, College of Medicine, The Catholic University of Korea, Seoul, Korea
ABSTRACT Clinical genomic diagnosis is unfamiliar to many dermatologists. Limited knowledge of bioinformatics has limited the use of the next generation sequencing method in dermatological clinics. We evaluated the usefulness of whole genome sequencing as a diagnostic approach to inherited dermatological disease. Here, we present our experience with two female siblings with atypical familial generalized lipodystrophy with diabetes mellitus and dyslipidemia. Whole genome sequencing was performed to diagnose the inherited disease. We compared control genomic databases using the Exome Aggregation Consortium, and filtered false-positive calls with the segmental duplication, non-flagged single nucleotide variants and COSMIC mutation databases, and applied the prediction tools of SIFT and PolyPhen2. The two siblings who presented with generalized lipodystrophy were diagnosed with an atypical progeroid syndrome with a p.D136H mutation in the LMNA gene (NM_005572). We diagnosed a familial atypical progeroid syndrome using whole genome sequencing. In this paper, we present our experience with whole genome sequencing and demonstrate that it can provide useful information for clinical genomic diagnosis of inherited diseases with atypical clinical features, such as atypical progeroid syndrome.
Key words: atypical progeroid syndrome, genomic diagnosis, LMNA mutation, next generation sequencing, whole genome sequencing.
INTRODUCTION There have been significant technical advances in the decade since the Human Genome Project was completed in 2003. Next generation sequencing (NGS) provides a much cheaper and higher throughput alternative to sequencing DNA and can be completed in just a few days. A new era of clinical genomics that identifies genes responsible for inherited skin diseases has begun.1 However, data analysis of NGS, especially whole genome sequencing (WGS), may be difficult for dermatologists because it requires specialized knowledge of bioinformatics to obtain accurate information from sequencing data. Here, we report on two female siblings who presented with generalized fat loss that was suspicious for generalized lipodystrophy. The clinical features of the probands were quite different with previously reported generalized lipodystrophy. We performed WGS to diagnose the inherited disease and to identify genes responsible for the disease.
METHODS Case presentation A 27-year-old Korean female was born with normal weight and showed normal growth and development until she was
11 years old. She reported that generalized fat loss had gradually progressed and she was diagnosed with diabetes mellitus when she was 16 years old. She had difficulties in controlling blood sugar levels. There was abnormal lipid metabolism with serum cholesterol of 520 mg/dL and serum triglycerides of more than 3000 mg/dL. She was 160 cm tall and weighed 30 kg. Prominent fat loss was observed on the face, extremities, trunk, back, palms and soles. No breast tissue was observed. She had no acanthosis nigricans, hyperpigmentation or hypertrichosis, which are clinical characteristics of generalized lipodystrophy (Fig. 1a, left and upper right). The patient reported that permanent teeth had not erupted and she only had several remaining deciduous teeth. The patient reported thin hair and mild hair loss since she was 10 years old. However, she did not have a highpitched voice, scleroderma, osteoporosis, acro-osteolysis, cataract, hypertension or psychiatric disturbance. Her sister presented with the same clinical features, including generalized subcutaneous fat loss, diabetes mellitus and dyslipidemia, and the same dental problem. She was much shorter than her sister (143 cm tall) and weighed 33 kg. Generalized fat loss started when she was 13 years old (Fig. 1a, middle and lower right). She had thin hair; however, she did not report hair loss yet. She had no osteoporosis, cataract or
Correspondence: Young Bok Lee, M.D., Ph.D., Department of Dermatology, College of Medicine, Uijeongbu St Mary’s Hospital, The Catholic University of Korea, 271 Chunbo Street, Uijeongbu-si, Gyeonggi-do 480-717, Korea. Email: [email protected] Received 4 March 2015; accepted 20 May 2015.
© 2015 Japanese Dermatological Association
1149
S. Lee et al.
Patients
(a)
The siblings were enrolled into our study after they consulted the Department of Dermatology at Uijeongbu St Mary’s Hospital. Informed consent was provided by the patients. All of the procedures were performed with the approval of the ethics committee of Uijeongbu St Mary’s Hospital (Kyunggi, Korea; institutional review board no. UC14TISI0122).
WGS and variant calling Genomic DNA was extracted from peripheral blood leukocytes, which underwent library preparation using the TruSeq Nano DNA Sample Preparation Kit (Illumina, San Diego, CA, USA) and paired-end sequencing through the Illumina HiSeq X Ten platform (150 bp in length). We aligned generated sequence reads to the human reference genome (hg19) and called genomic variants under default options using the Isaac aligner and variant caller, respectively. Variant annotation was performed using the ANNOVAR program.2
(b)
Variant filtration and discovery of causal mutations Among the sequence variants called, we selected pathogenic candidates according to several filtration criteria as follows: (i) non-silent single nucleotide variants (SNV) or indels; (ii) allele frequency (AF) of less than 10 4 in the 1000 Genomes Project, Exome Sequencing Project and Exome Aggregation Consortium (ExAC, URL: http://exac.broadinstitute.org), and Complete Genomics sequencing data;3,4 (iii) not located in the segmental duplication regions; (iv) neither non-flagged SNV (dbSNP 138) nor COSMIC mutations (cosmic 70); and (v) predicted to be damaging by SIFT and PolyPhen2.5,6 ClinVar was used to screen previously reported disease- or phenotype-related variants among candidates.7 For the validation of final candidates, we sequenced the target region from the affected sister using PCR and subsequent Sanger sequencing methods (forward primer, GAGGCAAGCAGATGCAAACC; reverse primer, GGACAGGTGAATGGCTCTGAA).
Figure 1. (a) Clinical features of the sisters and (b) pedigree of the probands. Arrow indicates the proband.
psychiatric disturbance. Their mother also had experienced generalized fat loss and diabetes mellitus, and had passed away at the age of 40 years due to complications associated with diabetic mellitus (Fig. 1b). The probands were clinically diagnosed with generalized lipodystrophy with diabetes mellitus; however, the clinical characteristics of the probands were not consistent with familial generalized lipodystrophy that had been previously reported. We performed WGS for the older sister to identify the gene responsible for the inherited disease and sequenced the target region from the younger sister using polymerase chain reaction (PCR) and subsequent Sanger sequencing methods.
RESULTS The sequencing summary is shown in Table 1. Mean read depth was 34.8X and 95.7% of genomic regions were covered by 20 or more reads. For the 3.91 million variants called, we applied stepwise filtrations described in Methods (Fig. 2) and found one variant, rs267607619 (LMNA p.D136H, Fig. 3), which was previously reported to be causative for atypical progeroid
Table 1. Summary of whole genome sequencing
Sample
Total reads
De-duplicated reads
Patient
762 369 890
707 727 844
Mappable reads
Mean depth (9)
% ≥910 coverage
% ≥920 coverage
% ≥930 coverage
SNV
Small insertions
Small deletions
663 027 384
34.8
98.3
95.7
77.2
3 451 965
221 744
240 944
SNV, single nucleotide variant.
1150
© 2015 Japanese Dermatological Association
WGS of Korean APS with LMNA mutation
Figure 2. Stepwise filtration of candidate genes. SNV, silent single nucleotide variant. syndrome in an Indian woman.8 This variant was also listed in the ClinVar database, but its clinical significance was categorized as “untested”.
DISCUSSION The present study details our experience with the first Korean case of atypical progeroid syndrome and proposes an easy and efficient method of WGS data analysis for use in dermatology. Initially, we had difficulty with the clinical diagnosis of two patients presenting with generalized fat loss. With the results of genomic diagnosis, we successfully identified atypical progeroid syndrome using WGS and bioinformatics. WGS revealed an accurate genomic diagnosis identifying an LMNA p.D136H mutation (NM_005572) in a few days. This LMNA p.D136H mutation was the first report in a Korean family with atypical progeroid syndrome. The LMNA p.D136H
mutation was reported in a previous report in an Indian patient.8 The clinical manifestations of the previous case were quite similar to our case, including generalized fat loss and diabetes mellitus, dyslipidemia and onset of disease. However, the previous case was caused by a de novo mutation without family history. The LMNA p.D136H mutation has not been reported other than in the previous Indian case and is classified as “untested” in the ClinVar database. The presenting cases provided meaningful evidence that the LMNA p.D136H mutation has pathogenicity that results in a presentation of generalized lipodystrophy with severe metabolic syndromes. The LMNA gene encodes lamin A/C, which are structural components of the nuclear lamina and nucleoplasmic scaffold and are involved in maintaining nuclear shape and structure. Mutations in the LMNA gene are responsible for diseases of striated muscle, lipodystrophy syndrome, peripheral neuropathy and progeria. Among the progeria syndromes, Hutchinson– Gilford progeria syndrome is a well-recognized typical autosomal dominant progeria due to LMNA gene mutation and characterized by growth retardation, alopecia, loss of subcutaneous fat, and cutaneous, skeletal9 and cardiovascular features.10 Besides Hutchinson–Gilford progeria syndrome, several atypical progeria syndromes with LMNA gene mutations have been reported with various clinical features, such as abnormal lipid metabolism and distribution, osteoporosis or acro-osteolysis.8 Atypical progeroid syndrome is a rare disease and it has been reported in Chinese11 and Japanese populations.12 Although the LMNA mutations have been noted in previously reported cases,11,12 the clinical characteristics were different from the present case. In this case, we diagnosed a Korean family with atypical progeroid syndrome by detecting an LMNA mutation using the WGS method. In diagnosing a rare inherited disease with various clinical features, such as atypical progeroid syndrome, WGS can contribute useful information for diagnosis. Successful discovery of the casual mutations for rare diseases largely depends on the annotation databases used. First, pathogenic candidates may differ slightly according to the choice of gene database. There are several gene annotation databases available, such as RefSeq, Ensembl and UCSC gene sets, and we used the most conservative one, the RefSeq gene database. Second, the sample size of normal healthy controls is very important for discovery of the mutations in rare diseases. In this study of our two patients, we used control
Figure 3. Genetic findings of the patient’s sequencing analysis in this family revealed a p.D136H mutation of the LMNA gene.
© 2015 Japanese Dermatological Association
1151
S. Lee et al.
genomic databases including ExAC, which provides the exomic variant information for more than 60 000 unrelated individuals. In addition, the use of several genomic databases allows for more filtering of false-positive calls. We used the segmental duplication, non-flagged SNV and COSMIC mutation database to exclude false variant calls, and we applied the prediction tools of SIFT and PolyPhen2 to exclude biologically benign or tolerant variants (Fig. 2). Using the strategy described above, this study successfully identified the causal mutation in a patient in a few days (only 1 day after variant calling). However, it is essential that each of the criteria can be modified depending on the situation. For example, if there are too many candidates left after filtration, the AF criteria may need to be stricter in order to leave even rarer variants. If we applied an AF cut-off of 10 6 instead of 10 4 to our patient, 10 variants remained, which still included the LMNA p.D136H mutation. Likewise, use of different prediction tools may also result in different candidates. In particular, if interested in indentifying pathogenic coding indels, most prediction tools are of little help at all because they cover only non-synonymous SNV. Therefore, although the application of high-throughput sequencing technology in clinical diagnostics has become much easier and faster than ever, minor titrations or curations by bioinformaticians may always be necessary for both higher sensitivity and specificity. In our experience, WGS served as a powerful tool for identifying the responsible gene in an inherited dermatological disease. Analysis takes only a few days using stepwise filtration, which may save time with the concept of a 2-day genome analysis.13 We hope that more genes can be identified in inherited dermatological disease using the NGS technique and the pathogenicity of candidate genes can be verified by dermatologists.
ACKNOWLEDGMENTS: The authors would like to thank the Exome Aggregation Consortium and the groups that provided exome variant data for comparison. A full list of contributing groups can be
1152
found at http://exac.broadinstitute.org/about. There was no funding sources.
CONFLICT OF INTEREST:
None declared.
REFERENCES 1 Grada A, Weinbrecht K. Next-generation sequencing: methodology and application. J Invest Dermatol 2013; 133: e11. 2 Wang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res 2010; 38: e164. 3 Genomes Project C, Abecasis GR, Auton A et al. An integrated map of genetic variation from 1,092 human genomes. Nature 2012; 491: 56–65. 4 Drmanac R, Sparks AB, Callow MJ et al. Human genome sequencing using unchained base reads on self-assembling DNA nanoarrays. Science 2010; 327: 78–81. 5 Kumar P, Henikoff S, Ng PC. Predicting the effects of coding nonsynonymous variants on protein function using the SIFT algorithm. Nat Protoc 2009; 4: 1073–1081. 6 Adzhubei IA, Schmidt S, Peshkin L et al. A method and server for predicting damaging missense mutations. Nat Methods 2010; 7: 248–249. 7 Landrum MJ, Lee JM, Riley GR et al. ClinVar: public archive of relationships among sequence variation and human phenotype. Nucleic Acids Res 2014; 42: D980–D985. 8 Garg A, Subramanyam L, Agarwal AK et al. Atypical progeroid syndrome due to heterozygous missense LMNA mutations. J Clin Endocrinol Metab 2009; 94: 4971–4983. 9 Maggi L, D’Amico A, Pini A et al. LMNA-associated myopathies: the Italian experience in a large cohort of patients. Neurology 2014; 83: 1634–1644. 10 Lai CC, Yeh YH, Hsieh WP et al. Whole-exome sequencing to identify a novel LMNA gene mutation associated with inherited cardiac conduction disease. PLoS One 2013; 8: e83322. 11 Guo H, Luo N, Hao F, Bai Y. p.Pro4Arg mutation in LMNA gene: a new atypical progeria phenotype without metabolism abnormalities. Gene 2014; 546: 35–39. 12 Motegi S, Yokoyama Y, Uchiyama A et al. First Japanese case of atypical progeroid syndrome/atypical Werner syndrome with heterozygous LMNA mutation. J Dermatol 2014; 41: 1047–1052. 13 Saunders CJ, Miller NA, Soden SE et al. Rapid whole-genome sequencing for genetic disease diagnosis in neonatal intensive care units. Sci Transl Med 2012; 4: 154ra35.
© 2015 Japanese Dermatological Association
